Synthetic data for determining health of a network security system

• US 10,693,749 B2
• Filed: 05/17/2016
• Issued: 06/23/2020
• Est. Priority Date: 06/05/2015
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Prasad, Rohit Chandra, Viswanathan, Bharathwaj Sankara, Nguyen, Hoang Viet, Jeyakumar, Vimalkumar, Spadaro, Roberto Fernando, Malhotra, Varun Sagar, Yadav, Navindra
• Primary Examiner(s): Cribbs, Malcolm
• Assistant Examiner(s): Holmes, Angela R
• Application Number:
  US15/157,300
• Publication Number:
  US 20160359878A1
• Time in Patent Office: 1,498 Days
• Field of Search: 726 1
• CPC Class Codes: G06F 16/122 using management policies backup systems G06F11/1446; file migration policies for HSM systems G06F16/185
• CPC Class Codes: G06F 16/137 Hash-based content-based indexing of textual data G06F16/31
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/17 Details of further file system functions
• CPC Class Codes: G06F 16/173 Customisation support for file systems, e.g. localisation, multi-language support, personalisation
• CPC Class Codes: G06F 16/174 Redundancy elimination performed by the file system management of the data involved in backup or backup restore using de-duplication of the data G06F11/14
• CPC Class Codes: G06F 16/1744 using compression, e.g. sparse files
• CPC Class Codes: G06F 16/1748 De-duplication implemented within the file system, e.g. based on file segments de-duplication techniques in storage systems for the management of data blocks G06F3/0641
• CPC Class Codes: G06F 16/2322 using timestamps
• CPC Class Codes: G06F 16/235 Update request formulation
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 2009/4557 Distribution of virtual machine instances; Migration and load balancing
• CPC Class Codes: G06F 2009/45587 Isolation or security of virtual machine instances
• CPC Class Codes: G06F 2009/45591 Monitoring or debugging support
• CPC Class Codes: G06F 2009/45595 : Network integration; Enabling network access in virtual machine instances
• CPC Class Codes: G06F 21/53 : by executing in a restricted environment, e.g. sandbox or secure virtual machine
• CPC Class Codes: G06F 21/552 : involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/556 : involving covert channels, i.e. data leakage between processes inhibiting the analysis of circuitry or operation with measures against power attack G06F21/755
• CPC Class Codes: G06F 21/566 : Dynamic detection, i.e. detection performed at run-time, e.g. emulation, suspicious activities
• CPC Class Codes: G06F 2221/033 : Test or assess software
• CPC Class Codes: G06F 2221/2101 : Auditing as a secondary aspect
• CPC Class Codes: G06F 2221/2105 : Dual mode as a secondary aspect
• CPC Class Codes: G06F 2221/2111 : Location-sensitive, e.g. geographical location, GPS
• CPC Class Codes: G06F 2221/2115 : Third party
• CPC Class Codes: G06F 2221/2145 : Inheriting rights or properties, e.g., propagation of permissions or restrictions within a hierarchy
• CPC Class Codes: G06F 3/0482 : Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04847 : Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 9/45558 : Hypervisor-specific management and integration aspects
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 99/00 : Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06T 11/206 : Drawing of charts or graphs
• CPC Class Codes: H04J 3/0661 : using timestamps
• CPC Class Codes: H04J 3/14 : Monitoring arrangements for SDH/SONET rings H04J3/085
• CPC Class Codes: H04L 1/242 : by comparing a transmitted test signal with a locally generated replica
• CPC Class Codes: H04L 41/046 : comprising network management agents or mobile agents therefor
• CPC Class Codes: H04L 41/0668 : by dynamic selection of recovery network elements, e.g. replacement by the most appropriate element after failure
• CPC Class Codes: H04L 41/0803 : Configuration setting
• CPC Class Codes: H04L 41/0806 : for initial configuration or provisioning, e.g. plug-and-play
• CPC Class Codes: H04L 41/0816 : the condition being an adaptation, e.g. in response to network events
• CPC Class Codes: H04L 41/0893 : Assignment of logical groups to network elements
• CPC Class Codes: H04L 41/0894 : Policy-based network configuration management
• CPC Class Codes: H04L 41/12 : Discovery or management of network topologies
• CPC Class Codes: H04L 41/16 : using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/22 : comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 41/40 : using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 43/02 : Capturing of monitoring data
• CPC Class Codes: H04L 43/026 : using flow identification
• CPC Class Codes: H04L 43/04 : Processing captured monitoring data, e.g. for logfile generation
• CPC Class Codes: H04L 43/045 : for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/062 : related to network traffic
• CPC Class Codes: H04L 43/08 : Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
• CPC Class Codes: H04L 43/0805 : by checking availability
• CPC Class Codes: H04L 43/0811 : by checking connectivity
• CPC Class Codes: H04L 43/0829 : Packet loss
• CPC Class Codes: H04L 43/0841 : Round trip packet loss
• CPC Class Codes: H04L 43/0858 : One way delays
• CPC Class Codes: H04L 43/0864 : Round trip delays
• CPC Class Codes: H04L 43/0876 : Network utilisation, e.g. volume of load or congestion level
• CPC Class Codes: H04L 43/0882 : Utilisation of link capacity
• CPC Class Codes: H04L 43/0888 : Throughput
• CPC Class Codes: H04L 43/10 : Active monitoring, e.g. heartbeat, ping or trace-route
• CPC Class Codes: H04L 43/106 : using time related information in packets, e.g. by adding timestamps
• CPC Class Codes: H04L 43/12 : Network monitoring probes
• CPC Class Codes: H04L 43/16 : Threshold monitoring
• CPC Class Codes: H04L 43/20 : the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV
• CPC Class Codes: H04L 45/306 : Route determination based on the nature of the carried application
• CPC Class Codes: H04L 45/38 : Flow based routing
• CPC Class Codes: H04L 45/46 : Cluster building
• CPC Class Codes: H04L 45/507 : Label distribution
• CPC Class Codes: H04L 45/66 : Layer 2 routing, e.g. in Ethernet based MAN's
• CPC Class Codes: H04L 45/74 : Address processing for routing
• CPC Class Codes: H04L 47/11 : Identifying congestion
• CPC Class Codes: H04L 47/20 : Traffic policing
• CPC Class Codes: H04L 47/2441 : relying on flow classification, e.g. using integrated services [IntServ]
• CPC Class Codes: H04L 47/2483 : involving identification of individual flows
• CPC Class Codes: H04L 47/28 : in relation to timing considerations
• CPC Class Codes: H04L 47/31 : by tagging of packets, e.g. using discard eligibility [DE] bits
• CPC Class Codes: H04L 47/32 : by discarding or delaying data units, e.g. packets or frames
• CPC Class Codes: H04L 61/5007 : Internet protocol [IP] addresses
• CPC Class Codes: H04L 63/0227 : Filtering policies mail message filtering H04L51/212
• CPC Class Codes: H04L 63/0263 : Rule management
• CPC Class Codes: H04L 63/06 : for supporting key management in a packet data network cryptographic mechanisms or cryptographic arrangements for key management H04L9/08
• CPC Class Codes: H04L 63/0876 : based on the identity of the terminal or configuration, e.g. MAC address, hardware or software configuration or device fingerprint
• CPC Class Codes: H04L 63/1408 : by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1416 : Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 : Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 : Vulnerability analysis
• CPC Class Codes: H04L 63/1441 : Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/145 : the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
• CPC Class Codes: H04L 63/1458 : Denial of Service
• CPC Class Codes: H04L 63/1466 : Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
• CPC Class Codes: H04L 63/16 : Implementing security features at a particular protocol layer
• CPC Class Codes: H04L 63/20 : for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1001 : for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/51 : Discovery or management thereof, e.g. service location protocol [SLP] or web services
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/75 : Indicating network or usage conditions on the user display
• CPC Class Codes: H04L 69/16 : Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
• CPC Class Codes: H04L 69/22 : Parsing or analysis of headers
• CPC Class Codes: H04L 7/10 : Arrangements for initial synchronisation
• CPC Class Codes: H04L 9/0866 : involving user or device identifiers, e.g. serial number, physical or biometrical information, DNA, hand-signature or measurable physical characteristics
• CPC Class Codes: H04L 9/3239 : involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD
• CPC Class Codes: H04L 9/3242 : involving keyed hash functions, e.g. message authentication codes [MACs], CBC-MAC or HMAC
• CPC Class Codes: H04W 72/54 : based on quality criteria
• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

An example method can include choosing a pattern or patterns of network traffic. This pattern can be representative of a certain type of traffic such as an attack. The pattern can be associated with various components of a network and can describe expected behavior of these various components. A system performing this method can then choose a nodes or nodes to generate traffic according to the pattern and send an instruction accordingly. After this synthetic traffic is generated, the system can compare the behavior of the components with the expected behavior. An alert can then be created to notify an administrator or otherwise remedy any problems.

Claims

1. A computer-implemented method comprising:

capturing network traffic data and associated data using at least a first sensor of a first virtual machine of a network, a second sensor of a first server hosting the first virtual machine, and a third sensor of a first networking device connected to the first server;

determining a pattern in the network traffic data and the associated data, the pattern associated with respective expected behavior for at least the first sensor, the second sensor, and the third sensor;

determining a plurality of selected nodes of the network for generating data corresponding to the pattern, the plurality of selected nodes including at least a second virtual machine of the network, a second server hosting the second virtual machine, and a second networking device connected to the second server, the second virtual machine executing a fourth sensor, the second server executing a fifth sensor, and the second networking device executing a sixth sensor;

causing each of the plurality of selected nodes to generate a respective portion of the data corresponding to the pattern; and

comparing respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor to the respective expected behavior of the first sensor, the second sensor, and the third sensor.

2. The computer-implemented method of claim 1, further comprising:

determining a plurality of patterns in the network traffic data and the associated data; and

causing respective data corresponding to each of the plurality of patterns to be generated over a specified period of time.

3. The computer-implemented method of claim 2, further comprising:

selecting a random order for the respective data corresponding to each of the plurality of patterns to be generated from a configured set of patterns.

4. The computer-implemented method of claim 1, further comprising:

selecting a random port of at least one node of the plurality of selected nodes from which to generate the respective portion of the data corresponding to the pattern.

5. The computer-implemented method of claim 1, wherein the pattern corresponds to at least one of a DDoS attack, a MAC address spoofing, a port knock, or normal traffic.

6. The computer-implemented method of claim 1, wherein a packet of pattern includes a flag indicating that the packet is a synthetic packet.

7. The computer-implemented method of claim 1, further comprising:

determining that the respective actual behavior of at least one of the fourth sensor, the fifth sensor, or the sixth sensor does not correspond to the respective expected behavior of at least one of the first sensor, the second sensor, and the third sensor; and

sending an alert that the respective actual behavior of the at least one of the fourth sensor, the fifth sensor, or the sixth sensor does not correspond to the respective expected behavior of the at least one of the first sensor, the second sensor, or the third sensor.

8. The computer-implemented method of claim 1, further comprising:

determining that the respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor corresponds to the respective expected behavior of the first sensor, the second sensor, and the third sensor; and

providing information to a presentation module that the respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor corresponds to the respective expected behavior for the first sensor, the second sensor, and the third sensor.

9. A non-transitory computer-readable medium comprising instructions stored thereon that, when executed, cause a computing device to:

capture network traffic data and associated data using at least a first sensor of a first virtual machine of a network, a second sensor of a first server hosting the first virtual machine, and a third sensor of a first networking device connected to the first server;

determine a pattern in the network traffic data and the associated data, the pattern associated with respective expected behavior for at least the first sensor, the second sensor, and the third sensor;

determine a plurality of selected nodes of the network for generating data corresponding to the pattern, the plurality of selected nodes including at least a second virtual machine of the network, a second server hosting the second virtual machine, and a second networking device connected to the second server, the second virtual machine executing a fourth sensor, the second server executing a fifth sensor, and the second networking device executing a sixth sensor;

cause each of the plurality of selected nodes to generate a respective portion of the data corresponding to the pattern; and

compare respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor to the respective expected behavior of the first sensor, the second sensor, and the third sensor.

10. The non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, further cause the computing device to:

determine a plurality of patterns in the network traffic data and the associated data; and

cause respective data corresponding to each of the plurality of patterns to be generated over a specified period of time.

11. The non-transitory computer-readable medium of claim 10, wherein the instructions, when executed, further cause the computing device to:

select a random order for the respective data corresponding to each of the plurality of patterns to be generated from a configured set of patterns.

12. The non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, further cause the computing device to:

select a random port of at least one node of the plurality of selected nodes from which to generate the respective portion of the data corresponding to the pattern.

13. The non-transitory computer-readable medium of claim 9, wherein the pattern corresponds to at least one of a DDoS attack, a MAC address spoofing, or a port knock.

14. The non-transitory computer-readable medium of claim 9, wherein a packet of pattern includes a flag indicating that the packet is a synthetic packet.

15. The non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, further cause the computing device to:

determine that the respective actual behavior of at least one of the fourth sensor, the fifth sensor, or the sixth sensor does not correspond to the respective expected behavior of at least one of the first sensor, the second sensor, and the third sensor; and

send an alert that the respective actual behavior of the at least one of the fourth sensor, the fifth sensor, or the sixth sensor does not correspond to the respective expected behavior of the at least one of the first sensor, the second sensor, or the third sensor.

16. The non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, further cause the computing device to:

determine that the respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor corresponds to the respective expected behavior of the first sensor, the second sensor, and the third sensor; and

provide information to a presentation module that the respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor corresponds to the respective expected behavior for the first sensor, the second sensor, and the third sensor.

17. A system comprising:

a processor; and

a memory including instructions that, when executed by the processor, cause the system to;

capture network traffic data and associated data using at least a first sensor of a first virtual machine of a network, a second sensor of a first server hosting the first virtual machine, and a third sensor of a first networking device connected to the first server;

determine a pattern in the network traffic data and the associated data, the pattern associated with respective expected behavior for at least the first sensor, the second sensor, and the third sensor;

determine a plurality of selected nodes of the network for generating data corresponding to the pattern, the plurality of selected nodes including at least a second virtual machine of the network, a second server hosting the second virtual machine, and a second networking device connected to the second server, the second virtual machine executing a fourth sensor, the second server executing a fifth sensor, and the second networking device executing a sixth sensor;

cause each of the plurality of selected nodes to generate a respective portion of the data corresponding to the pattern; and

compare respective actual behavior of the fourth sensor, the fifth sensor, and the sixth sensor to the respective expected behavior of the first sensor, the second sensor, and the third sensor.

18. The system of claim 17, wherein the policy is a whitelist rule, and the instructions further cause the system to:

determine a plurality of patterns in the network traffic data and the associated data; and

cause respective data corresponding to each of the plurality of patterns to be generated over a specified period of time.

19. The system of claim 17, wherein the policy is a whitelist rule, and the instructions further cause the system to:

select a random order for the respective data corresponding to each of the plurality of patterns to be generated from a configured set of patterns.

20. The system of claim 17, wherein the policy is a whitelist rule, and the instructions further cause the system to:

select a random port of at least one node of the plurality of selected nodes from which to generate the respective portion of the data corresponding to the pattern.